Pinosylvin as a bioactive stilbene is of great interest for food supplements and pharmaceuticals development. In comparison to conventional extraction of pinosylvin from plant sources, biosynthesis engineering of microbial cell factories is a sustainable and flexible alternative method. Current synthetic strategies often require expensive phenylpropanoic precursor and inducer, which are not available for large-scale fermentation process. In this study, three bioengineering strategies were described to the development of a simple and economical process for pinosylvin biosynthesis in Escherichia coli. Firstly, we evaluated different construct environments to give a highly efficient constitutive system for enzymes of pinosylvin pathway expression: 4-coumarate: coenzyme A ligase (4CL) and stilbene synthase (STS). Secondly, malonyl coenzyme A (malonyl-CoA) is a key precursor of pinosylvin bioproduction and at low level in E. coli cell. Thus clustered regularly interspaced short palindromic repeats interference (CRISPRi) was explored to inactivate malonyl-CoA consumption pathway to increase its availability. The resulting pinosylvin content in engineered E. coli was obtained a 1.9-fold increase depending on the repression of fabD (encoding malonyl-CoA-ACP transacylase) gene. Eventually, a phenylalanine over-producing E. coli consisting phenylalanine ammonia lyase was introduced to produce the precursor of pinosylvin, trans-cinnamic acid, the crude extraction of cultural medium was used as supplementation for pinosylvin bioproduction. Using these combinatorial processes, 47.49 mg/L pinosylvin was produced from glycerol.

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key to virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against phosphoglycerate dehydrogenase (PHGDH), proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.

The cyclin M (CNNM) family of Mg 2+ transporters is reported to promote tumour progression by binding to phosphatase of regenerating liver (PRL) proteins. Here, we established an assay for detection of the binding between the cystathionine-beta-synthase (CBS) domain of human CNNM3 (a region responsible for PRL binding) and human PRL2 using fluorescence resonance energy transfer (FRET) techniques. By fusing YPet to the C-terminus of the CNNM3 CBS domain and CyPet to the N-terminus of PRL2, we performed a FRET-based binding assay with purified proteins in multiwell plates and successfully detected the changes in fluorescence intensity derived from FRET with a reasonable K d . We then confirmed that the addition of non-YPet-tagged CNNM3 and non-CyPet-tagged PRL proteins inhibited the changes in FRET intensity, whereas non-YPet-tagged CNNM3 with a mutation at the PRL2-binding site did not exhibit such inhibition. Furthermore, newly synthesized peptides derived from the CNNM loop region, with the PRL-binding sequences of the CNNM3 CBS domain, inhibited the interactions between CNNM3 and PRL2. Overall, these results showed that this method can be used for screening to identify inhibitors of CNNM-PRL interactions, potentially for novel anticancer therapy.

Chemical space exploration is a major task of the hit-finding process during the pursuit of novel chemical entities. Compared with other screening technologies, computational de novo design has become a popular approach to overcome the limitation of current chemical libraries. Here, we reported a de novo design platform named systemic evolutionary chemical space explorer (SECSE). The platform was conceptually inspired by fragment-based drug design, that miniaturized a “lego-building” process within the pocket of a certain target. The key of virtual hits generation was then turned into a computational search problem. To enhance search and optimization, human intelligence and deep learning were integrated. Application of SECSE against PHGDH, proved its potential in finding novel and diverse small molecules that are attractive starting points for further validation. This platform is open-sourced and the code is available at http://github.com/KeenThera/SECSE.